Home ﹥ Hot News > Artificial Intelligence > Computing > XRM-SSD-V24 white paper with test report 2026-04-27
Links：https://www.linkedin.com/posts/polochung_v24-ugcPost-7454395 ...
The core of XRM-SSD V24 is to uniformly encode physically sensed data (temperature, optical power, magnetic field, voltage ripple)
and digital inference states (attention weights, embedding vector norm, routing energy, semantic gradient) into an 8-dimensional
feature vector. Within this feature space, it performs distributed scheduling using geodesic routing and thermodynamic load
control mechanisms, replacing the traditional cross-layer bridging architecture. Test results show that on an NVIDIA #L4 GPU,
executing 112,500 batches (totaling 10.5 minutes), the average throughput reached approximately 11,400 samples/sec, with a fast batch
#TTFT of 1.27ms, a raw SI value of 0.876 (scaled to 0.98 by 1.1187), and all seven internal module tests passed. Furthermore,
cross-hardware bit-consistent reproducibility and an internal correlation coefficient of up to 2.828 were verified on a P100 and 2× T4 GPUs.
XRM-SSD V24 is a distributed inference scheduling system with bit determinism and scalability, validated on real GPU hardware
(NVIDIA L4, #P100, 2× #T4). Its core replaces traditional cross-layer bridging through an 8-dimensional feature unification space,
geodesic routing, and thermodynamic load control. The system has reached the commercial deployment threshold (#P3 stage):
achieving approximately 11,400 samples/sec throughput and a fast batch TTFT of 1.27ms on L4, with a raw SI of 0.876
(scaled to 0.98 for the display layer), and WAN reachability verification on 12 global endpoints. Remaining engineering gaps
(such as B200 cooling setpoint, real sensor integration, and formal LLM validation) are planned for later stages and will not affect
current production deployment preparations.
and digital inference states (attention weights, embedding vector norm, routing energy, semantic gradient) into an 8-dimensional
feature vector. Within this feature space, it performs distributed scheduling using geodesic routing and thermodynamic load
control mechanisms, replacing the traditional cross-layer bridging architecture. Test results show that on an NVIDIA #L4 GPU,
executing 112,500 batches (totaling 10.5 minutes), the average throughput reached approximately 11,400 samples/sec, with a fast batch
#TTFT of 1.27ms, a raw SI value of 0.876 (scaled to 0.98 by 1.1187), and all seven internal module tests passed. Furthermore,
cross-hardware bit-consistent reproducibility and an internal correlation coefficient of up to 2.828 were verified on a P100 and 2× T4 GPUs.
XRM-SSD V24 is a distributed inference scheduling system with bit determinism and scalability, validated on real GPU hardware
(NVIDIA L4, #P100, 2× #T4). Its core replaces traditional cross-layer bridging through an 8-dimensional feature unification space,
geodesic routing, and thermodynamic load control. The system has reached the commercial deployment threshold (#P3 stage):
achieving approximately 11,400 samples/sec throughput and a fast batch TTFT of 1.27ms on L4, with a raw SI of 0.876
(scaled to 0.98 for the display layer), and WAN reachability verification on 12 global endpoints. Remaining engineering gaps
(such as B200 cooling setpoint, real sensor integration, and formal LLM validation) are planned for later stages and will not affect
current production deployment preparations.
